System and method for image reconstruction

ABSTRACT

A system and method relating to image processing are provided. The method may include the following operations. First data at a first bed position and second data at a second bed position may be received. The first bed position and the second bed position may have an overlapping region. A first image and a second image may be reconstructed based on the first data and the second data, respectively. Third data and fourth data corresponding to the overlapping region may be extracted from the first data and the second data, respectively. Merged data may be generated by merging the third data and the fourth data. A third image may be reconstructed based on the merged data. A fourth image may be generated through image composition based on the first image, the second image, and the third image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/225,915, filed on Aug. 2, 2016, which claimspriority of Chinese Patent Application No. 201510603686.8 filed on Sep.21, 2015 and Chinese Patent Application No. 201610200285.2 filed on Mar.31, 2016, the entire contents of each of which are hereby incorporatedby reference.

TECHNICAL FIELD

This present disclosure relates to image processing, and moreparticularly, relates to a system and method for data processing inimage reconstruction.

BACKGROUND

Positron emission tomography (PET) has been widely used in medicine fordiagnosis and other purposes. An object, such as a patient, may bescanned with a PET system to obtain PET datasets. For a ring tomography,the raw line-of-response (LOR) may be unevenly spaced. The LORs near thecenter of the PET ring may be wider than those far away from the centerof the PET ring. Conventional reconstruction algorithms may act on datawhich be pre-processed into corrected, evenly spaced histograms;however, such pre-processing may corrupt the characteristics, e.g.,Poisson statistics. The axial length of the detector may be smaller thanthe length of the patient under examination, besides, at one bedposition, an image obtained may be with a non-uniform SNR in differentregion.

SUMMARY

An aspect of the present disclosure relates to an image processingsystem. The system may include a processor and a non-transitory computerreadable storage medium. When computer executable instructions areexecuted, the processor may be caused to perform following operations.First data at a first bed position and second data at a second bedposition may be received, wherein the first and second bed position mayhave an overlapping region. A first image may be reconstructed based onthe first data. A second image may be reconstructed based on the seconddata. Third data corresponding to the overlapping region may beextracted from the first data. Fourth data corresponding to theoverlapping region may be extracted from the second data. Merged datamay be generated by merging the third data and the fourth data. A thirdimage may be reconstructed based on the merged data. And a fourth imagemay through image composition base on the first image, the second imageand the third image.

Another second aspect of the present disclosure relates to an imageprocessing method. The method may include one or more of the followingoperations. First data at a first bed position and second data at asecond bed position may be received, wherein the first and second bedposition may have an overlapping region. A first image may bereconstructed based on the first data. A second image may bereconstructed based on the second data. Third data corresponding to theoverlapping region may be extracted from the first data. Fourth datacorresponding to the overlapping region may be extracted from the seconddata. Merged data may be generated by merging the third data and thefourth data. A third image may be reconstructed based on the mergeddata. And a fourth image may through image composition base on the firstimage, the second image and the third image.

In some embodiments, the generating of the fourth image may includeweighted composition based on a first weight coefficient for the firstimage, a second weight coefficient for the second image, and a thirdweight coefficient for the third image.

In some embodiments, a sum of the first weight coefficient, the secondweight coefficient, and the third weight coefficient may be a fixedvalue.

In some embodiments, the first data, the second data, the third data andthe fourth data may be saved in a sinogram mode or a listmode.

In some embodiments, the first data, the second data, or the merged datamay be corrected.

In some embodiments, the merged data may be arranged based on angle ortime.

In some embodiments, the imaging system may be a Positron EmissionTomography (PET) system.

Still another aspect of the present disclosure relates to an imageprocessing system. The system may include a processor and anon-transitory computer readable storage medium. When computerexecutable instructions are executed, the processor may be caused toperform following operations. An image in a first coordinate system maybe obtained. A lookup table specifying a correlation between the firstcoordinate system and a second coordinate system may be obtained. Thevalue of a target pixel in a first dimension in the second coordinatesystem may be calculated based on the lookup table and an originalpixels of the image in the first coordinate system.

Still another aspect of the present disclosure relates to an imageprocessing method. The method may include one or more of the followingoperations. a) An image in a first coordinate system may be obtained. b)A lookup table specifying a correlation between the first coordinatesystem and a second coordinate system may be obtained. c) The value of atarget pixel in a first dimension in the second coordinate system may becalculated based on the lookup table and an original pixels of the imagein the first coordinate system.

In some embodiments, each entry in the lookup table may include aninteger part and a decimal part.

In some embodiments, the integer part of the entry in the lookup tablemay include an index of original pixel relating to the left boundary ofa target pixel. In some embodiments, the decimal part of the entry inthe lookup table may include a ratio of a first term to a second term.In some embodiments, the first term may be relative to the position of atarget pixel relative to the position of a corresponding original pixel,and the second term may be relative to the pixel size of thecorresponding original pixel.

In some embodiments, the operations may further include calculating acount sum. The count sum may be a sum of a count of a pixel and countsof all pixels before the pixel in the original coordinate system.

In some embodiments, the count sum may be used to calculate the value ofa target pixel in a target coordinate system.

In some embodiments, the first coordinate system may bemulti-dimensional. The operations further include performing b) and c)for each dimension of the first coordinate system.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a block diagram of an imaging system 100 according to someembodiments of the present disclosure;

FIG. 2 is a block diagram depicting a processor according to someembodiments of the present disclosure;

FIG. 3 illustrates a work flow for imaging processing according to someembodiments of the present disclosure;

FIG. 4 illustrates a process for image reconstruction according to someembodiments of the present disclosure;

FIG. 5 illustrates a process for an image transformation according tosome embodiments of the present disclosure;

FIG. 6 illustrates an image transformation in two different coordinatesystems according to some embodiments of the present disclosure;

FIG. 7A and FIG. 7B show a transformation of a single-dimensional arrayin two different coordinate systems according to some embodiments of thepresent disclosure;

FIG. 8 illustrates a process for creating a lookup table according tosome embodiments of the disclosure;

FIG. 9 shows a transformation of a two-dimensional array in twodifferent coordinate systems according to some embodiments of thepresent disclosure;

FIG. 10 illustrates a process for image composition according to someembodiments of the present disclosure; and

FIG. 11 shows a schematic of image composition according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of example in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “device,” “unit,” and/or“module” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theymay achieve the same purpose.

It will be understood that when a device, unit, or module is referred toas being “on,” “connected to” or “coupled to” another device, unit, ormodule, it may be directly on, connected or coupled to, or communicatewith the other device, unit, or module, or an intervening device, unit,or module may be present, unless the context clearly indicatesotherwise. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof. It willbe further understood that the terms “construction” and“reconstruction,” when used in this disclosure, may represent a similarprocess in which an image may be transformed from data. Moreover, thephrase “image processing” and the phrase “image generation” may be usedinterchangeably. In some embodiments, image processing may include imagegeneration.

FIG. 1 is a block diagram of an imaging system 100 according to someembodiments of the present disclosure. In some embodiments, the imagingsystem 100 may be a single modality imaging system, e.g., a DigitalSubtraction Angiography (DSA) system, a Magnetic Resonance Angiography(MRA) system, a Computed Tomography Angiography (CTA), a PositronEmission Tomography (PET) system, a Single Photon Emission ComputedTomography (SPECT) system, a Computed Tomography (CT) system, a DigitalRadiography (DR) system, etc. In some embodiments, the imaging systemmay be a multi-modality imaging system, e.g., a ComputedTomography-Positron Emission Tomography (CT-PET) system, a PositronEmission Tomography-Magnetic Resonance Imaging (PET-MRI) system, aSingle Photon Emission Computed Tomography-Positron Emission Tomography(SPECT-PET) system, a Digital Subtraction Angiography-Magnetic ResonanceImaging (DSA-MR) system, etc. For better understanding the presentdisclosure, a PET system may be described as an example of an imagingsystem. It should be noted that the imaging system 100 described belowis merely provided for illustration purposes, and not intended to limitthe scope of the present disclosure.

As illustrated in FIG. 1, the imaging system 100 may include an imagingdevice 110, a processor 120, a terminal 130, a display 140, and adatabase 150. The imaging device 110 may be configured to examine anobject and generate or provide one or more images of the object. In someembodiments, the imaging device 110 may include a gantry (not shown inFIG. 1). The object may be placed within the gantry during scanning. Insome embodiments, the imaging device 110 may include a bed to supportthe object. The object may include a substance, a cell, a tissue, anorgan, a part of or a whole body of a human or an animal. Otherexemplary embodiments may include but not limited to a man-madecomposition of organic and/or inorganic matters that are with or withoutlife. In some embodiments, the object may be a human patient. The humanpatient may lie on the back, lie in prone, sit, and stand within thegantry or in front of the imaging device.

The imaging device 110 may include a detector. The detector may includea plurality of detector blocks. A detector block may include a pluralityof detector elements. In some embodiments, the detector block may beflat or arc-shaped. In some embodiments, the detector may include twodetector blocks. The two detector blocks may be parallel to each other.In some embodiments, the two detector blocks may be at an angle (forexample, an oblique angle or a right angle) with each other. In someembodiments, the two detector blocks may be positioned symmetricallyabout the center of the gantry. In some embodiments, the two detectorblocks may be positioned asymmetrical about the center of the gantry. Insome embodiments, the detector may include two or more detector blocks.For instance, the detector may include four detector blocks. The anglebetween adjacent detector blocks may be approximately 90 degrees. Insome embodiments, the detector may include a plurality of detectorsforming a ring or a cylinder. The axial length of the detector may be,e.g., 10 centimeters, 20 centimeters, 30 centimeters, 40 centimeters, 50centimeters, 60 centimeters, 70 centimeters, 80 centimeters, 90centimeters, 1 meter, 1.2 meters, 1.5 meters, 1.6 meters, 1.8 meters, 2meters, or longer than 2 meters.

In a PET system, PET tracer molecules may be introduced into the object.Positrons may be emitted by the PET tracer molecules. After moving adistance, e.g., 1 micrometer, the positrons may undergo annihilationswith the electrons and may generate gamma photons. This process may bereferred as a coincidence event. A coincidence event may be assigned toa line-of-response (LOR) joining the two relevant detector elements. Thegamma photons may be detected by the detector of the imaging devices110. The detector may produce electrical signals based on the detectedgamma photons. In some embodiments, the electrical signals may beamplified, digitized, filtered, or may be subject to other processes toform imaging data. As used herein, the term “imaging data” may refer tothe data that may be used to reconstruct an image of the object underexamination. In some embodiments, the axial length of the detector maybe smaller than the length of the object under examination, andtherefore, the object may be scanned in a one bed position mode or amulti-bed positions mode. Besides, in the one bed position mode, imagingdata detected by the detector elements toward the axial ends (i.e.toward the ends along the axial direction) of the detector may be lessthan those detected by the detector elements toward the axial center(i.e. toward the center along the axial direction) of the detector.Therefore, the imaging data acquired at the bed position may result inan image with a non-uniform signal noise ratio (SNR). The imaging dataobtained at different bed positions may be combined to form a compositeimage of the object. The composite image of the object may be with auniform or essentially uniform SNR.

In some embodiments, the number of the bed positions may be two. In someembodiments, the number of the bed positions may be larger than two,e.g., three, four, five, six, or more. For illustration purposes, thescenario using two bed positions may be described in the followingdescription. The order of bed positions to scan the object may bearbitrary. In some embodiments, the imaging device 110 may scan theobject at the first bed position at first and then at the second bedposition. In some embodiments, the imaging device 110 may scan theobject at the second bed position at first and then at the first bedposition. In some embodiments, the duration of scanning at the first bedposition may be the same as that at the second position. In someembodiments, the duration of scanning at the first position may bedifferent from that at the second position.

The processor 120 may be configured to process the imaging data from theimaging device 110. In some embodiments, the processor 120 may beconfigured to perform operations including, for example, datapreprocessing, image reconstruction, image correction, imagecomposition, lookup table creation, or the like, or any combinationthereof. In some embodiments, imaging data obtained in one coordinatesystem may need to be transformed into data in another coordinatesystem. The relationship between two coordinate systems may be set up bythe processor. In some embodiments, the processor 120 may be configuredto generate a control signal relating to the configuration of theimaging device 110. In some embodiments, the result generated by theprocessor 120 may be provided to other modules or units in the systemincluding, e.g., the terminal 130, the display 140, the database 150. Insome embodiments, the data from the processor 120 may be transmitted tothe database 150 for storing. In some embodiments, the data from theprocessor 120 may be displayed by the display 140.

In some embodiments, the processor 120 may include any processor-basedand/or microprocessor-based units. Merely by way of examples, theprocessor may include a microcontroller, a reduced instruction setcomputer (RISC), application specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an acornreduced instruction set computing (RISC) machine (ARM), or any othercircuit or processor capable of executing the functions describedherein, or the like, or any combination thereof. In some embodiments,the processor 120 may also include a memory. In some embodiments, thememory may include Random Access Memory (RAM). In some embodiments, thememory may include Read Only Memory (ROM). The processor that may beused in connection with the present system described herein are notexhaustive and are not limiting. Numerous other changes, substitutions,variations, alterations, and modifications may be ascertained to oneskilled in the art and it is intended that the present disclosureencompass all such changes, substitutions, variations, alterations, andmodifications as falling within the scope of the present disclosure.

In some embodiments, the terminal 130 may be configured to receiveinput. The terminal 130 may include, for example, a mobile device (e.g.,a smart phone, a tablet, a laptop computer, or the like), a personalcomputer, other devices, or the like, or any combination thereof. Otherdevices may include a device that may work independently, or aprocessing unit or processing module assembled in another device (e.g.,an intelligent home terminal). The terminal 130 may include an inputdevice, a control panel (not shown in FIG. 1), etc. The input device maybe a keyboard, a touch screen, a mouse, a remote controller, or thelike, or any combination thereof. An input device may includealphanumeric and/or other keys that may be inputted via a keyboard, atouch screen (for example, with haptics or tactile feedback), a voiceinput, an image input, an eye tracking input, a brain monitoring system,or any other comparable input mechanism. The input information receivedthrough the input device may be communicated to the processor 120 via,for example, a bus, for further processing. Another type of the inputdevice may include a cursor control device, such as a mouse, atrackball, or cursor direction keys to communicate direction informationand command selections to, for example, the processor 120 and to controlcursor movement on the display device.

In some embodiments, the terminal 130 may communicate with the imagingdevice 110. The data input from the terminal 130 may be transmitted tothe imaging device 110 to control some parameters of the imaging device110. The parameters may include the position and the tilted angle of thebed in the imaging device 110, the scan duration, the scan times, or thelike, or any combination thereof. In some embodiments, the terminal 130may communicate with the processor 120. The terminal 130 may provideinstructions to the processor 120 for the instructions to be processedin the processor 120. For example, the instructions relating to the bedpositions, the reconstruction algorithm, the correction algorithm, etc.,may be provided via the terminal 130. In some embodiments, the terminal130 may communicate with the display 140 and the database 150. The datafrom the terminal 130 may be transmitted to the database 150 forstoring.

The display 140 may be configured to display information. Exemplaryinformation for display may include an image, a request for input orparameters relating to image acquisition and/or processing, etc. Thedisplay 140 may include a liquid crystal display (LCD), a light emittingdiode (LED) based display, a flat panel display or curved screen (ortelevision), a cathode ray tube (CRT), or the like, or any combinationthereof.

The database 150 may be configured to store data. The data may be fromthe imaging device 110, the processor 120, the terminal 130, or othermodules or units in the system. Exemplarity data may include imagingdata from the imaging device 110, a lookup table, a reconstructed image,etc. In some embodiments, the database 150 may be a hard disk drive. Insome embodiments, the database 150 may be a solid-state drive. In someembodiments, the database 150 may be a removable storage drive. Merelyby way of examples, a non-exclusive list of removable storage drive thatmay be used in connection with the present disclosure includes a flashmemory disk drive, an optical disk drive, or the like, or anycombination thereof.

In some embodiments, the imaging device 110, the processor 120, theterminal 130, the display 140 and the database 150 may be connected toor communicate with each other directly. In some embodiments, theimaging device 110, the processor 120, the terminal 130, the display140, and the database 150 may be connected to or communicate with eachother via a network. In some embodiments, the imaging device 110, theprocessor 120, the terminal 130, the display 140, and the database 150may be connected to or communicate with each other via an intermediateunit (not shown in FIG. 1). The intermediate unit may be a visiblecomponent or an invisible field (radio, optical, sonic, electromagneticinduction, etc.). The connection between different units may be wired orwireless. The wired connection may include using a metal cable, anoptical cable, a hybrid cable, an interface, or the like, or anycombination thereof. The wireless connection may include using a LocalArea Network (LAN), a Wide Area Network (WAN), a Bluetooth, a ZigBee, aNear Field Communication (NFC), or the like, or any combination thereof.The network that may be used in connection with the present systemdescribed herein are not exhaustive and are not limiting.

In some embodiments, the processor 120, the database 150, the display140, or the terminal 130 may be located near the imaging device 110. Inother embodiments, one or more of the above components may be remotefrom the imaging device 110. Merely by way for example, the processor120 and the database 150 may be implemented on a cloud platform. Thecloud platform may be a cloud computing platform or a cloud storingplatform. The model of the cloud platform may include a private cloud, apublic cloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof. Asanother example, the display 140, and the terminal 130 may be operatedby a remote medical system.

It should be noted that the above description about the imaging systemis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the imagingsystem 100 may include several processors, databases, displays,terminals when the imaging system 100 is a multi-modality system. Asanother example, the display 140, the terminal 130, and the processor120 may be integrated as one device. However, those variations andmodifications do not depart from the scope of the present disclosure.

FIG. 2 is a block diagram depicting a processor according to someembodiments of the present disclosure. The processor 120 may include anacquisition unit 210, a reconstruction unit 220, a composition unit 230,and a correction unit 240. The acquisition unit 210 may be configured toacquire the imaging data. In some embodiments, the imaging data mayinclude imaging data produced in different bed positions. In someembodiments, there may be one or more overlapping regions betweendifferent bed positions. A portion of an object under examination may bescanned more than one time in some scenarios. For illustration purposes,an overlapping region may refer to the portion of an object that may bescanned more than one time at the different bed positions; anoverlapping region may also refer to a same region (of an object)included in multiple images or imaging data taken at different bedpositions. First overlapping imaging data may refer to imaging data ofthe overlapping regions produced at a first bed position. Secondoverlapping imaging data may refer to imaging data of the overlappingregion produced at a second bed position. In some embodiments, the firstoverlapping imaging data and the second overlapping imaging data may bemerged to form merged imaging data. The merged imaging data may bereconstructed to generate an image of the overlapping region. In someembodiments, the imaging data may be from the imaging device 110 and/orthe database 150. The imaging data may be in a listmode format or in asinogram format.

The reconstruction unit 220 may be configured to reconstruct imagingdata to generate one or more images. The imaging data may bereconstructed by using a reconstruction algorithm. The reconstructionalgorithm may be an analytic reconstruction algorithm, an iterativereconstruction algorithm, or based on compressed sensing (CS). Theanalytic reconstruction algorithm may include a filtered back projection(FBP) algorithm, a back projection filtration (BFP) algorithm, ap-filtered layergram, or the like. The iterative reconstructionalgorithm may include an ordered subset expectation maximization (OSEM)algorithm, a maximum likelihood expectation maximization (MLEM)algorithm, etc.

In some embodiments, the reconstruction unit 220 may include an imagetransformation unit 250. The image transformation unit 250 may beconfigured to transform an image from a first coordinate system to asecond coordinate system. In some embodiments, the first coordinatesystem and the second coordinate system may be uniformly distributed,i.e., the first and second coordinate system may be linearly scaled. Insome embodiments, the first coordinate system and the second coordinatesystem may be non-uniformly distributed. In some embodiments, one of thefirst coordinate system and the second coordinate system may beuniformly distributed, and the other may be non-uniformly distributed.

The composition unit 230 may be configured to composite two or moreimages to form a composite image. In some embodiments, the images to becomposited may have an overlapping region. In some embodiments, theimages to be composited may have no overlapping region. The images maybe 3D images, 2D images, etc. The composite image may be a 3D image, a2D image, etc. In some embodiments, the size of the images to be cornposited may be the same or different. For a PET system, an object underexamination may be scanned at several bed positions to generate imagesof the object. In some embodiments, there may be an overlapping regionbetween two adjacent bed positions and a portion of the object may bescanned at the two bed positions. At each bed position, an image may begenerated. Images generated at two adjacent bed positions may include anoverlapping region. For a first image taken at a first bed position anda second image taken at a second bed position, the overlapping ratio ofthe overlapping region with respect to a first image or a second imagemay be any value from 0 to 1, e.g., 10%, 20%, 30%, 40%, 50%, 60%, etc.As used herein, an overlapping ratio may refer to the ratio of the sizeof an overlapping region to the size of an image including theoverlapping region. The size may be evaluated in terms of area, length,etc. In some embodiments, for a scan with multiple overlapping regions,the overlapping ratios may be fixed in a scan. In some embodiments, fora scan with multiple overlapping regions, the overlapping ratio may beadjustable during a scan. In some embodiments, for a scan with multipleoverlapping regions, at least two overlapping ratios may be the same. Insome embodiments, for a scan with multiple overlapping regions, at leasttwo overlapping ratios may be different. In some embodiments, differentscans may generate overlapping regions of different sizes. In someembodiments, different scans may generate overlapping regions of a samesize. In some embodiments, the sizes of overlapping regions may beadjusted for different scans.

For illustration purposes, scenarios with two bed positions aredescribed in detail below. First imaging data and a first image may begenerated at a first bed position. Second imaging data and a secondimage may be generated at a second bed position. In some embodiments,there may be an overlapping region between the two bed positions; eitherof the two images generated at the two bed positions may include anoverlapping region.

In some embodiments, first overlapping imaging data corresponding to theoverlapping region in the first image may be extracted from the firstimage data; second overlapping imaging data corresponding to theoverlapping region in the second image may be extracted from the secondimaging data. The first overlapping imaging data and the secondoverlapping imaging data may be merged to form merged imaging data. Themerged imaging data may be reconstructed to generate an image of theoverlapping region, also referred to as a third image. The first image,the second image, and the third image may be composited to generate acomposite image of the object. In some embodiments, the overlappingregion of the first image, the overlapping region of the second imageand the third image may be multiplied by a weight coefficient to form animage of the object.

The correction unit 240 may be configured to correct imaging data. Thecorrection method may include random correction, scatter correction,attenuation correction, dead time correction, uniformity correction, orthe like, or any combination thereof.

It should be noted that the above description about the processor ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. In some embodiments, theprocessor may include other modules or units. For example, some modulesmay be omitted or integrated with other module. As another example, astorage may be added in the processor to save data. However, thosevariations and modifications do not depart from the scope of the presentdisclosure.

FIG. 3 is a flowchart illustrating a process for an imaging systemaccording to some embodiments of the present disclosure. In step 310,one or more parameters may be set. Exemplary parameters may include aheight of the bed, a rotation angle of an image, a position of the bedduring scanning, a scanning duration, or the like, or any combinationthereof. In a multi-bed position scanning mode, exemplary parameters mayfurther include the number of the bed positions, different bedpositions, a scanning order of the different bed positions, whether toinclude one or more overlapping regions, a size and/or a position of anoverlapping region, or the like, or any combination thereof.

In step 320, imaging data may be acquired. This step may be performed bythe acquisition unit 210 in FIG. 2. In some embodiments, the imagingdata may be acquired in the one bed position mode or the multi-bedpositions mode. In the multi-bed positions mode, the imaging dataacquired may include first imaging data when the bed is at a first bedposition and second imaging data when the bed is at a second bedposition. The imaging data may be in a listmode format or in a sinogramformat.

In step 330, the imaging data may be reconstructed. Step 330 may beperformed by the reconstruction unit 220 in FIG. 2. In some embodiments,the imaging data may be reconstructed using a reconstruction algorithmas described elsewhere in the present disclosure. In some embodiments,during reconstruction, an image may be transformed from a firstcoordinate system to a second coordinate system. Detailed description ofan exemplary image transformation may be described in FIG. 5.

In step 340, images may be composited. In some embodiments, step 340 maybe performed by the composition unit 230 illustrated in FIG. 2. For amulti-bed positions mode, the imaging data may include first imagingdata when the bed is at a first bed position and second imaging datawhen the bed is at a second bed position. In some embodiments, the firstbed position and the second bed position may overlap. In someembodiments, the first imaging data may be reconstructed to form a firstimage and the second imaging data may be reconstructed to form a secondimage. In some embodiments, the first image and the second image may becomposited to form a composite image. In some embodiments, the imagingdata corresponding to the overlapping region of the first image may bereferred to as third imaging data, and the imaging data corresponding tothe overlapping region of the second image may be referred to as fourthimaging data. In some embodiments, the third imaging data and the fourthimaging data may be merged together to form merged imaging data. Themerged imaging data may be reconstructed using a reconstructionalgorithm, and an image of the overlapping region may be obtained. Insome embodiments, the first image, the second image, and the image ofthe overlapping region may be used to produce a composite image.

It should be noted that the above description about the process for animaging system is merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. In someembodiments, other steps may be added in the process. For anotherexample, the intermediated data and/or the final data of the process maybe stored in the process, and the storage location may be in database150 or other modules or units capable of storing data. In someembodiments, the imaging data acquired in step 320 may be corrected bythe correction unit 240 before being reconstructed. However, thosevariations and modifications do not depart from the scope of the presentdisclosure.

FIG. 4 shows an exemplary process of image reconstruction according tosome embodiments of the present disclosure. It should be noted that animage reconstruction algorithm described is merely an example, and notintended to limit the scope of the present disclosure.

In step 401, one or more reconstruction parameters may be set. Exemplaryreconstruction parameters may include a bed position mode (e.g., singlebed position mode, multi-bed position mode, etc.), a height of the bed,a rotation angle of an image, the number of rotation angles, a positionof the bed during scanning, a scanning duration, an iteration, thenumber of subsets, a system model, or the like, or any combinationthereof. In a multi-bed positions mode, exemplary parameters may furtherinclude number of bed positions, different bed positions, a scanningorder of the different bed positions, an overlapping region, the sizeand position of an overlapping region, or the like, or any combinationthereof. An initial image estimate of the object may be obtained in step401. In some embodiments, each subset may include projections at severalrotation angles. In some embodiments, each subset may includeprojections at same number of rotation angles. In some embodiments, eachsubset may include projections at different number of rotation angles.For example, the first subset may include projections at three rotationangles, and the second subset may include projections at four rotationangles. The number of subsets may be an arbitrary value, including,e.g., 4, 8, 16.

For illustration purposes, an exemplary iterative reconstruction isdescribed below. In some embodiments, the iterative reconstructionalgorithm may include a maximum-likelihood expectation-maximization(MLEM) algorithm, an ordered-subsets (OSEM) algorithm, anattenuation-weighted maximum-likelihood expectation-maximization(AW-MLEM) algorithm, an attenuation-weighted ordered-subsets (AW-OSEM)algorithm, a line-of-response maximum-likelihoodexpectation-maximization (LOR-MLEM) algorithm, a line-of-responseordered-subsets (LOR-OSEM) algorithm, or the like, or any combinationthereof. Merely by way for example, in the embodiments using LOR-OSEMalgorithm, the initial image estimate of the object may be updated for acertain number of times. The number of the update times may be equal tothe number of the iteration times set in step 401. In some embodiments,an image in each iteration may be rotated with a rotation angle set instep 401. In a PET system, a LOR (line-of-response) may refer to a lineof coincidence connecting a pair of relevant detector elements of thedetector. For a ring tomography, raw LORs may be unevenly spaced. TheLORs near the center along the circumferential direction of the PET ringmay be wider than those far away from the center of the PET ring. Toreconstruct an image using raw LORs, the image may need to betransformed from an original coordinate system (also called a firstcoordinate system) to a target coordinate system (also called a secondcoordinate system).

In step 402, a forward rotation may be performed on the image with apresent rotation angle set in step 401. In step 403, the image may betransformed from the image space to the LOR space. In step 404, aforward projection may be performed. In step 405, a division may beperformed to generate a correction factor. In step 406, a backwardprojection may be performed based on the correction factor. In step 407,image may be performed from the LOR space to the image space. In step408, a backward rotation may be performed. In step 409, whether thepresent rotation angle is the last angle may be judged. If the presentrotation angle is not the last angle, result of the backward projectionafter the image transformation may be summed up in step 410, and thenthe process may return to step 402. If the present rotation angle is thelast angle, a determination may be made as to whether the present subsetis the last subset in step 411. If the present subset is not the lastsubset, the image may be updated in step 412, and then the process mayreturn to step 402. If the present subset is the last subset, adetermination may be made as to whether the present iteration is thelast iteration in step 413. If the present iteration is not the lastiteration, the process may return to step 402. If the present iterationis the last iteration, the reconstructed image may be generated in step414.

It should be noted that the above description about the process of imagereconstruction is merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. In someembodiments, other steps may be added in the process, for example, theintermediated data and/or the final data of the process may be stored inthe process, and the storage location may be in database 150 or othermodules or units capable of storing data. However, those variations andmodifications do not depart from the scope of the present disclosure.

FIG. 5 is a flowchart illustrating an image transformation according tosome embodiments of the present disclosure. In step 510, a first imagedata in a first coordinate system may be acquired. As used herein, theterm “image data” may refer to the values of pixels, texels, surfels, orvoxels of an image. The first image data may be one-dimensional,two-dimensional, three-dimensional, or the like, or any combinationthereof. In some embodiments, step 510 may be performed by theacquisition unit 210 of the processor 120.

In step 520, second image data in a second coordinate system may begenerated based on the first image data and a lookup table. Thedimension of the second image data may be the same with that of thefirst image data. The first coordinate system or the second coordinatesystem may be a one-dimensional coordinate system, a two-dimensionalcoordinate system, a three-dimensional coordinate system, or afour-dimensional coordinate system, etc. In some embodiments, the firstcoordinate system may be an S-T coordinate system and the secondcoordinate system may be an X-Y coordinate system. Merely by way forexample, a value in the S-axis with respect to a PET ring may representa distance from a LOR at an angle to the center of the PET ring; a valuein the T-axis with respect to a PET ring may represent the angle betweena LOR and a horizontal plane or between a LOR and a vertical plane. Insome embodiments, the first coordinate system may be an X-Y coordinatesystem, and the second coordinate system may be an S-T coordinatesystem.

In some embodiments, the first coordinate system and the secondcoordinate system may be parallel to each other. That is, the X-axis inthe second coordinate system may be parallel to the S-axis in the firstcoordinate system, and the Y-axis in the second coordinate system may beparallel to the T-axis in the first coordinate system. In someembodiments, the first coordinate system and the second coordinatesystem may be non-parallel to each other. In some embodiments, the firstcoordinate system and the second coordinate system may be uniformlydistributed. In some embodiments, part of the first coordinate system orthe second coordinate system may be non-uniformly distributed. Merely byway of example, the X-axis and the Y-axis may be uniformly distributed,and the S-axis and the T-axis may be non-uniformly distributed.

The lookup table may store a relationship between the first coordinatesystem and the second coordinate system. Based on the lookup table, thedata in the first coordinate system may be mapped into the secondcoordinate system. In some embodiments, a certain configuration detectormay correspond to a certain lookup table. If the configuration changes,the lookup table may change correspondingly. For a PET ring, the lookuptable may relate to many factors including, for example, the radius ofthe PET ring, the gap between two adjacent detector blocks, or the like,or a combination thereof. In some embodiments, the lookup table may bestored in the database 150. Detailed description about the process ofsetting up a lookup table may be described in FIG. 8.

It should be noted that the above description about the process for animaging system is merely provided for the purposes of illustration, andnot intended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. In someembodiments, other steps may be added in the process. For example,intermediated data and/or the final data of the process may be stored inthe database 150 or other modules or units capable of storing data.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 6 shows an image transformation in two different coordinate systemsaccording to some embodiments of the present disclosure. Forillustration purposes, image transformation in one dimension may bedescribed below, and not intended to limit the scope of the presentdisclosure. The pixel values in the first coordinate system, e.g., theS-T coordinate system, may be transformed to that in the secondcoordinate system, e.g., the X-Y coordinate system. In a one-dimensionalsystem, the pixel size may be represented by the length of a pixel. In atwo-dimensional system, the pixel size may be represented by the area ofa pixel. In some embodiments, the pixel value may be an average valuefor the pixel size. A pixel in the second coordinate system (called atarget pixel) may relate to one or more pixels in the first coordinatesystem (called an original pixel). Based on the original pixels relatingto a target pixel and the pixel size of the target pixel, the targetpixel value may be obtained.

As shown in FIG. 6, pixels g₁, g₂, and g₃ are in the S-T coordinatesystem, and pixels f₁, f₂, and f₃ are in the X-Y coordinate system. Insome embodiments, the pixels in the S-T coordinate system may bedesignated as the original pixels, and the pixels in the X-Y coordinatesystem may be designated as the target pixels. It should be noted thatthe indexes of the original pixels and the target pixels used herein aremerely for the purpose of convenience, and not intended to be limiting.As shown in FIG. 6, the left boundary of the pixel f₂ may be indicatedby x₁. The right boundary of the pixel f₂ may be indicated by x₂. Theleft boundary of the pixel g₂ may be indicated by s₁. The right boundaryof the pixel g₂ may be indicated by s₂. The pixel size of the pixel g₂may be s₂-s₁, and the pixel size of the pixel f₂ may be x₂-x₁. The leftboundary of the pixel f₂ may be between the left boundary and the rightboundary of the pixel g₁. The right boundary of the pixel f₂ may bebetween the left boundary and the right boundary of the pixel g₃.Therefore, the pixel f₂ may relate to the pixels g₁, g₂ and g₃. Thevalue of the pixel f₂ may be obtained based on the pixels g₁, g₂, and g₃and the pixel size of the pixel f₂. In some embodiments, the value ofthe pixel f₂ may be described as the expression below:

$\begin{matrix}{{{f\lbrack 2\rbrack} = \frac{{( {s_{1} - x_{1}} ) \cdot {g\lbrack 1\rbrack}} + {( {s_{2} - s_{1}} ) \cdot {g\lbrack 2\rbrack}} + {( {x_{2} - s_{2}} ) \cdot {g\lbrack 3\rbrack}}}{x_{2} - x_{1}}},} & (1)\end{matrix}$where f[2] may represent the value of pixel f₂, g[1] may represent thevalue of the pixel g₁, g[2] may represent the value of the pixel g₂,g[3] may represent the value of the pixel g₃, (x₂-x₁) may represent thesize of pixel f₂, (s₂-s₁) may represent the size of pixel g₂, (s₁-x₁)may represent the difference between the left boundary of the pixel f₂and the right boundary of the pixel g₁, and (x₂-s₂) may represent thedifference between the right boundary of the pixel f₂ and the leftboundary of the pixel g₃. The position relationship between the targetpixel(s) and the original pixel(s) may be obtained based on a lookuptable. In some embodiments, depending on the lookup table, the index ofan original pixel corresponding to the left boundary of the target pixelmay be obtained, and the distance between the left boundary of thetarget pixel and the left boundary of the original pixel may also beobtained. Based on the position relationship, the value of the targetpixel may be estimated.

It should be noted that the above description about an imagetransformation is merely an example, and is not intended to be limiting.Various alterations, improvements, and modifications may occur and areintended to those skilled in the art, though not expressly statedherein. For persons having ordinary skills in the art, the number of thepixels can be varied arbitrarily and the relative position of the pixelsin the first coordinate system and the second coordinate systemaccording to some embodiments of the present disclosure. In someembodiments, a target pixel may related one or more original pixels.These alterations, improvements, and modifications are intended to besuggested by this disclosure, and are within the spirit and scope of theexemplary embodiments of this disclosure.

FIG. 7A and FIG. 7B show a transformation in one dimension of an imagebetween two different coordinate systems according to some embodimentsof the present disclosure. The data of an image in one dimension may berepresented by a one-dimensional array. It should be noted that theone-dimensional array used herein is merely for the purpose ofconvenience, and not intended to be limiting. In some embodiments, thearrays may be two-dimensional, three-dimensional, or even higherdimensional. The following description may show the transformation fromthe S-T coordinate system to the X-Y coordinate system.

In the S-T coordinate system, the values of original pixels in the Sdirection may be presented by the array g[j], j=0, 1, . . . N, wherein Nmay be an integer. The right boundary of the pixels may be representedby s₂, . . . , s_(j)+1. In the X-Y coordinate system, the values oftarget pixels in the X direction may be represented by the array f[i],i=0, 1, . . . , M, wherein M may be an integer. The right boundary ofthe pixels may be represented by x₁, x₂, . . . , x_(i+1). Based on alookup table, the values of target pixels may be estimated. In someembodiments, the lookup table may be stored in the database 150. In someembodiments, the lookup table may be set up in real time. The lookuptable may be configured to record the position of the left boundary ofeach target pixel A in the S direction of the S-T coordinate system. Thevalues in the lookup table may be represented by the array xLut[i],wherein i=0, 1, . . . , i_(max), wherein i_(max) may represent the totalnumber of the pixels in the S direction of the S-T coordinate system.For a one-dimensional coordinate system, the lookup table may be aone-dimensional array. For a two-dimensional coordinate system, thelookup table may be a two-dimensional array. For a three-dimensionalcoordinate system, the lookup table may be a three-dimensional array.

A value in the lookup table may include an integer part and a decimalpart. The integer part may represent the index j of a pixel g_(i),wherein the left boundary of the target pixel f_(i) may be no smallerthan the left boundary of the original pixel g_(i), and may be smallerthan the right boundary of the original pixel g_(i), i.e., s_(i) ≤x_(i)<s_(j+1). The decimal part may represent a ratio of a firstdifference to a second difference. The first difference may be betweenthe left boundary of the target pixel f_(i) and the left boundary of theoriginal pixel g_(j), i.e., x_(i)-s_(j). The second difference may bebetween the right boundary of original pixel g_(j) and the left boundaryof original pixel g_(j), i.e., the length of the original pixel g_(j),s_(j+1)−s_(j). As shown in FIG. 7B, the left boundary of the targetpixel f₀ may be equal to that of the original pixel g₀, i.e., x₀-s₀.Apparently for persons having ordinary skills in the art, afterunderstanding the basic principles of image transformation, the form anddetails may be modified or varied without departing from the principles.For example, the left boundary of the target pixel f₀ may be differentfrom that of the original pixel g₀. The modifications and variations arestill within the scope of the current disclosure described above.

To obtain the value of a target pixel f based on the lookup table, anoriginal pixel g_(j) may be obtained, wherein s_(j)≤x_(i)<s_(j+1). Then,sumg[j] may be calculated based on the expression below

$\begin{matrix}{{{sumg}\lbrack j\rbrack} = \{ {\begin{matrix}0 & {j = 0} \\{\sum\limits_{k = 0}^{j - 1}\;{{g\lbrack k\rbrack} \cdot ( {s_{k + 1} - s_{k}} )}} & {j > 0}\end{matrix},} } & (2)\end{matrix}$where g[k]·(s_(k+1)−s_(k)) may represent the count of a pixel g_(k), andsumg[i], referred to as a count sum, may represent the sum of the countof the pixel g_(j−1) and counts of all pixels before the pixel g_(j−1)in the original coordinate system. A pixel before the pixel g_(j−1) mayrepresent the pixel whose index may be smaller than (j−1).

Then, the value of the target pixel A may be obtained based on theexpression below:

$\begin{matrix}{{{f\lbrack i\rbrack} = {\frac{1}{x_{i + 1} - x_{i}}\{ {{{sumg}\lbrack j_{1} \rbrack} - {{sumg}\lbrack j_{0} \rbrack} + {( {{{xLut}\lbrack {i + 1} \rbrack} - j_{1}} ) \cdot {g\lbrack j_{1} \rbrack} \cdot ( {s_{j_{1} + 1} - s_{j_{1}}} )} - {( {{{xLut}\lbrack i\rbrack} - j_{0}} ) \cdot {g\lbrack j_{0} \rbrack} \cdot ( {s_{j_{0} + 1} - s_{j_{0}}} )}} \}}},} & (3)\end{matrix}$where j₀=└xLut[i], j₁=└xLut[i+1]┘, and the square bracket └┘ mayrepresent rounded down to an integer.

In some embodiments, the lookup table may merely relate to the firstcoordinate system and the second coordinate system. Therefore, if thefirst coordinate system and the second coordinate system are certain, alookup table may be determined. In some embodiments, the storage spaceoccupied by the lookup table may merely relate to the number of pixelsin the target coordinate system, and not relating to the number ofpixels in the original coordinate system or values of pixels in thetarget or original coordinate system. The lookup table may beinsensitive to the difference between the pixel size in the originalcoordinate system and that in the target coordinate system. In someembodiments, the lookup table may be stored in the database 150, othermodules or units capable of storing. The lookup table may be used fortransformation of many images.

It should be noted that the terms “the left boundary” and “the rightboundary” are used for illustration purposes, and are not intended to belimiting. Various alterations, improvements, and modifications may occurand are intended to those skilled in the art, though not expresslystated herein. For example, for vertical directions, the left boundaryand the right boundary may be replaced by the lower boundary and theupper boundary. In general, the left boundary may represent the smallerboundary of a pixel with respect to a certain axis and the rightboundary may represent the larger boundary of a pixel with respect tothe axis. These alterations, improvements, and modifications areintended to be suggested by this disclosure, and are within the spiritand scope of the exemplary embodiments of this disclosure.

FIG. 8 shows a process of creating a lookup table in one-dimensionalsystem according to some embodiments of the disclosure. In step 810,parameters may be set. Merely by way of example, i=0, j=0, x₀=s₀, andxLut[i]=0, 0≤i<i_(max), in which the number i may represent the index ofa target pixel f_(i), and the number j may represent the index of anoriginal pixel g_(j).

In step 820, a determination may be made as to whether i is smaller thani_(max), in which i_(max) may represent the total number of targetpixels in an one-dimensional coordinate system. If i<i_(max), indicatingthe all target pixels in the second coordinate system have not beenmapped with original pixels in the first coordinate system, in step 830,a determination may be made as to whether the left boundary of thetarget pixel A is larger than or equal to the left boundary of anoriginal pixel g_(j), and is smaller than the right boundary of theoriginal pixel g_(j), i.e., whether s_(j)≤x_(i)<s_(j+1). Ifs_(j)≤x_(i)<s_(j+1), a value may be assigned to xLut[i]. The assignedvalue may include an integer part and a decimal part. The integer partmay be the index j of the original pixel g_(j). The decimal part may bea ratio of two terms. The first term may relate to the position oftarget pixel relative to the position of the original pixel. Forinstance, the first term may be the difference between the left boundaryof the target pixel f_(i) and the left boundary of the original pixelg_(j), i.e., x_(i)-s_(j). The second term may relate to the pixel sizeof the original pixel. For instance, the second term may be thedifference between the right boundary of original pixel g_(j) and theleft boundary of original pixel g_(j), i.e., the length of the originalpixel g_(j), s_(j+1)−s_(j). In some embodiments, in step 840, the indexof the target pixel may be added by 1, and the process may return tostep 820. In step 830, if the expression s_(j)≤x_(i)<s_(j+1) is notsatisfied, the index of the original pixel may be added by 1 in step850, and the process may return to step 830. In step 820, if i<i_(max)is not satisfied, indicating that the all target pixels in the secondcoordinate system have been mapped with original pixels in the firstcoordinate system, a lookup table may be generated in step 860. Based onthe lookup table and the expression (3), the value of a target pixel maybe obtained.

It should be noted that the above description about the process forcreating a lookup table is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. The process described in FIG. 8 is for a one-dimensionalcoordinate system. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. The process described in FIG. 8 may also beapplied to a two-dimensional coordinate system, a three-dimensionalcoordinate system, or other coordinate systems. For an n-dimensionalcoordinate system, a lookup table may be obtained by performing theprocess repeatedly as described in FIG. 8 with respect to eachdimension. In some embodiments, other steps may be added in the process.For example, intermediated data and/or the final data of the process maybe stored in the process, and the storage location may be in thedatabase 150 or other modules or units capable of storing data. In someembodiments, the lookup table obtained in step 860 may be stored in thedatabase 150. However, those variations and modifications do not departfrom the scope of the present disclosure.

FIG. 9 shows a transformation of a two-dimensional array in twodifferent coordinate systems according to some embodiments of thepresent disclosure. A two-dimensional image may be represented by atwo-dimensional array. In some embodiments, an image in the S-Tcoordinate system may be transformed to that in the X-Y coordinatesystem. As shown in FIG. 9, firstly, a lookup table, xLut, in thedirection corresponding to S-axis or X-axis may be generated. Based onthe lookup table, xLut, the two-dimensional array g[s, t] in the S-Tcoordinate system may be transformed to h[x, t] in the X-T coordinatesystem. Then, a lookup table, yLut, in the direction corresponding toT-axis or Y-axis may be generated. Based on the lookup table, yLut, thetwo-dimensional array h[x, t] in X-T coordinate system may betransformed to f[x, y] in the X-Y coordinate system. In someembodiments, a two-dimensional lookup table may be set up. Based on thetwo-dimensional lookup table, the two-dimensional array g[s, t] in theS-T coordinate system may be transformed to f[x, y] in the X-Ycoordinate system.

It should be noted that the size and the number of pixels shown in FIG.9 are merely provided for illustrating an example of imagetransformation, and not intended to limit the scope of the presentdisclosure. For example, the order of the transformation with respect toS-axis and T-axis may be exchanged, or the transformation with respectto S-axis and T-axis may be performed at the same time. As anotherexample, the two-dimensional coordinate system may be athree-dimensional coordinate system or other dimensional coordinatesystem. However, those variations and modifications do not depart fromthe scope of the present disclosure.

FIG. 10 is a flowchart of an exemplary process of image compositionaccording to some embodiments of the present disclosure. Merely by wayfor example, a PET image composition method may be described below, butnot intended to limit the scope of the present disclosure. In step 1010,first imaging data may be obtained. The first imaging data may be from,for example, the database 150 or the imaging device 110. The firstimaging data may correspond to a first bed position. In someembodiments, the first imaging data may be reconstructed to a first PETimage using a reconstruction algorithm as described elsewhere in thepresent disclosure. The reconstruction may be performed by thereconstruction unit 220. In some embodiments, the first PET image may beobtained from the database 150.

In step 1020, second imaging data may be obtained. The second imagingdata may be from, for example, the database 150 or the imaging device110. The second imaging data may correspond to a second bed position. Insome embodiments, the second imaging data may be reconstructed to asecond PET image using a reconstruction algorithm as described elsewherein the present disclosure. The reconstruction algorithm used herein maybe the same as or different from that in step 1010. The reconstructionmay be performed by the reconstruction unit 220. In some embodiments,the second PET image may be obtained from the database 150. It should benoted that the order of step 1010 and step 1020 may also be exchanged orthey may be performed at the same time.

In some embodiments, the first imaging data and the second imaging datamay be in a listmode format. A coincidence event may be recorded as (ia,ib, ra, rb), in which (ia, ib) may represent the circumferentialpositions of two detector elements that may detect a coincidence event,and (ra, rb) may represent the axial positions of two detector elementsthat detect the coincidence event. In some embodiments, the firstimaging data and the second imaging data may be in a sinogram format. Insome embodiments, the first bed position and the second bed position mayoverlap. A portion of an object under examination may be scanned twiceat the two bed positions.

In step 1030, third imaging data may be exacted from the first imagingdata. In some embodiments, the third imaging data may be fromcoincidence events occurred in the overlapping region at the first bedposition. The coincidence events may belong to two categories. The firstcategory may be that both of two gamma photons belonging to acoincidence event are detected by two detector elements that cover theoverlapping region. The second category may be that one of two gammaphotons belonging to a coincidence event is detected by a detectorelement that covers the overlapping region and the other gamma photon isdetected by a detector element that does not cover the overlappingregion. As used herein, a detector element covering the overlappingregion may mean that the detector element is located at approximatelythe same position as the overlapping region along the axial direction ofthe detector. For instance, a detector element covering the overlappingregion may indicate that the detector element is directly or almostdirectly above or below of the overlapping region. In some embodiments,the third imaging data may be from a portion of the coincidence eventsoccurred in the overlapping region in the first bed position. In someembodiments, the coincidence events occurred in the overlapping regionmay only include some or all of the coincidence events of the firstcategory. In some embodiments, the coincidence events occurred in theoverlapping region may only include some or all of the coincidenceevents of the second category. In some embodiments, part of thecoincidence events occurred in the overlapping region may only includethe coincidence events of the first category, and part of thecoincidence events in the second category. In some embodiments, thecoincidence events occurred in the overlapping region may includecoincidence events of the first category and coincidence events of thesecond category.

In step 1040, fourth imaging data may be exacted from the second imagingdata. In some embodiments, the fourth imaging data may be fromcoincidence events occurred in the overlapping region at the second bedposition. The description about the fourth imaging data may be similarto that of the third imaging data, and is not repeated here. In someembodiments, step 1030 and step 1040 may be performed in serial. In someembodiments, step 1030 and step 1040 may be performed simultaneously.The third imaging data and the fourth imaging data may be arrangedaccording to a factor including, for example, an angle of a relevantLOR, the sampling time, etc.

In step 1050, the third imaging data and the fourth imaging data may bemerged together to provide merged imaging data. The merged imaging datamay be in a listmode format or in a sinogram format. At different bedpositions, coincidence events occurred in an overlapping region may bedetected by different axial detector elements. Therefore, the thirdimaging data and the fourth imaging data may be transformed to a samecoordinate system before being merged.

In step 1060, the merged imaging data may be reconstructed by using areconstruction algorithm to generate a third PET image. In someembodiments, the reconstruction algorithm used herein may be the same asthe reconstruction algorithm used in step 1010 or step 1020. In someembodiments, the reconstruction algorithm used herein may be differentfrom the reconstruction algorithm used in step 1010 or step 1020. Insome embodiments, the overlapping region may be imaged or scanned by oneor more detector elements near one axial end of the detector at one bedposition, and therefore only part of coincidence events in theoverlapping region may be detected.

In step 1070, the first PET image, the second PET image, and the thirdPET image may be composited to generate a composite PET image. Thecomposite PET image may include three portions. The first portion may bethe same as the non-overlapping region of the first PET image. Thesecond portion may be the same as the non-overlapping region of thesecond PET image. The third portion may relate to all or part of theoverlapping region of the first PET image, the overlapping region of thesecond PET image, and the third PET image.

In some embodiments, the overlapping region of the first PET image andthe overlapping region of the second PET image may be not used togenerate the third portion of the composite PET image. In someembodiments, the overlapping region of the first PET image and theoverlapping region of the second PET image may be also used to generatethe composite PET image. In this case, the overlapping region of thefirst PET image, the overlapping region of the second PET image, and thethird PET image may be taken into consideration; the contribution ofeach to the composite image may be regulated or manipulated by way ofassigning a weight coefficient. The weight coefficient corresponding tothe overlapping region of the first PET image may be referred to as afirst weight coefficient, the weight coefficient corresponding to theoverlapping region of the second PET image may be referred to as asecond weight coefficient, and the weight coefficient corresponding tothe third PET image may be referred to as a third weight coefficient. Insome embodiments, the three weight coefficients may be constants. Insome embodiments, one or more of the three weight coefficients mayrelate to a factor including, for example, an overlapping ratio, the SNRof a relevant image, etc. In some embodiments, one or more of the threeweight coefficients may follow a polynomial function. The highest degreeof the polynomial function may be an arbitrary value including, e.g.,one, two, three, four. In some embodiments, the sum of the three weightcoefficients may be one.

It should be noted that the above description about the process of imagecomposition is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. In someembodiments, other steps may be added in the process. For example,intermediated data and/or the final data of the process may be stored inthe process, and the storage location may be in the database 150 orother modules or units capable of storing data. In some embodiments, theimaging data acquired in step 1010 and 1020 may be corrected by thecorrection unit 240 before being reconstructed. The correction mayinclude random correction, scatter correction, attenuation correction,dead time correction, uniformity correction, or the like, or anycombination thereof. Besides, the imaging device 110 may scan the objectin more than two bed positions. In some embodiments, there may be anoverlapping region between two adjacent bed positions. In someembodiments, there may be no overlapping region between two adjacent bedpositions. Merely by way of example, for scans at three bed positions,the first bed position and the second bed position may overlap, and thesecond position and the third position may also overlap. For scans atfour bed positions, the first bed position and the second bed positionmay overlap, the second position and the third position may not overlap,and the third bed position and the fourth bed position may overlap.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 11 shows a schematic of image composition according to someembodiments of the present disclosure. As shown in FIG. 11, image A mayrepresent a first PET image, image B may represent a second PET image,image C may represent a third PET image, and image (A+B+C) may representa composite PET image. The length of the image A may be L, and thelength of the image B may also be L. The overlapping region of the imageA may be of a length D, and the overlapping of the image B may also beof a length D. The distance between a pixel in the overlapping region ofan image and the edge of the image in the axial direction may be d. Insome embodiments, the overlapping region of the image A may bemultiplied by a weight coefficient μ_(A)(d), the overlapping region ofthe image B may be multiplied by a weight coefficient μB(d), and theimage C may be multiplied by a weight coefficient μc(d). In someembodiments, the sum of the three weight coefficients may be one, asdescribed below:μ_(A)(d)+μ_(B)(d)+μ_(C)(d)=1.  (4)

Exemplary μ_(A)(d), μ_(B)(d), and μ_(C)(d) may be determined as theexpressions below:

$\begin{matrix}{{\mu_{A}(d)} = \{ {\begin{matrix}0 & {0 \leq d < {0.5D}} \\{\frac{2d}{D} - 1} & {{0.5D} \leq d < D}\end{matrix},} } & (5) \\{{\mu_{B}(d)} = \{ {\begin{matrix}0 & {{0.5D} \leq d < D} \\{1 - \frac{2d}{D}} & {0 \leq d < {0.5D}}\end{matrix},{and}} } & (6) \\{{\mu_{C}(d)} = \{ {\begin{matrix}{2 - \frac{2d}{D}} & {{0.5D} \leq d < D} \\\frac{2d}{D} & {0 \leq d < {0.5D}}\end{matrix}.} } & (7)\end{matrix}$

It should be noted that the above description about image composition ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the numberof images to be composited may be arbitrary. The length of image to becomposited may be the same with or different from each other. In someembodiments, the weight coefficients may be relative to other factors.The relationship between the factors and the weight coefficients may bevaried or modified. However, those variations and modifications do notdepart from the scope of the present disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “unit,” “component,” “device” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2003, Perl,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties, and so forth, used to describe and claim certain embodimentsof the application are to be understood as being modified in someinstances by the term “about,” “approximate,” or “substantially.” Forexample, “about,” “approximate,” or “substantially” may indicate ±20%variation of the value it describes, unless otherwise stated.Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that mayvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system for image processing, the systemcomprising: a processor; and a non-transitory computer readable storagemedium storing computer-executable instructions that when executed causethe processor to perform operations comprising: receiving first data ofan object at a first bed position and second data of the object at asecond bed position; wherein the first bed position and the second bedposition having an overlapping region corresponding to a same portion ofthe object; reconstructing a first image based on the first data;reconstructing a second image based on the second data, a region in theat least one of the first image or the second image representing thesame portion of the object having a signal noise ratio (SNR) lower thana region in the at least one of the first image or the second imagerepresenting another portion of the object; extracting third datacorresponding to the overlapping region from the first data; extractingfourth data corresponding to the overlapping region from the seconddata, the third data and the fourth data representing at least a portionof the same portion of the object; merging the third data and the fourthdata to generate merged data; reconstructing a third image based on themerged data; and generating a fourth image of the object through imagecomposition based on the first image, the second image, and the thirdimage, wherein the fourth image having a substantially uniform SNR. 2.The system of claim 1, the generating a fourth image comprising weightedcomposition based on a first weight coefficient for the first image, asecond weight coefficient for the second image, and a third weightcoefficient for the third image.
 3. The system of claim 2, a sum of thefirst weight coefficient, the second weight coefficient, and the thirdweight coefficient being a fixed value.
 4. The system of claim 1, thefirst data, the second data, the third data, and the fourth data beingsaved in a sinogram mode or a listmode.
 5. The system of claim 1, theoperations further comprising correcting the first data, the seconddata, or the merged data.
 6. The system of claim 1, the operationsfurther comprising arranging the merged data based on angle or time. 7.The system of claim 1, the system being a Positron Emission Tomography(PET) system.
 8. A system for image processing, the system comprising: aprocessor; and a non-transitory computer readable storage medium storingcomputer-executable instructions that when executed cause the processorto perform operations comprising: a) obtaining image data including aplurality of original pixels in a first coordinate system; b) obtaininga lookup table specifying a correlation between the first coordinatesystem and a second coordinate system applied to a target image, thelookup table recording a position of each of a plurality of targetpixels in the target image in the first coordinate system, the positionof each of a plurality of target pixels being defined by positions ofone or more original pixels of the image data in the first coordinatesystem; and c) for each of the plurality of target pixels in the targetimage, calculating a value of the target pixel in the second coordinatesystem based on the lookup table and values of the one or more originalpixels of the image data in the first coordinate system.
 9. The systemof claim 8, each entry in the lookup table comprising an integer partand a decimal part.
 10. The system of claim 9, the integer part of theentry in the lookup table comprising an index of original pixel relatingto the left boundary of a target pixel.
 11. The system of claim 9, thedecimal part of the entry in the lookup table comprising a ratio of afirst term to a second term, the first term relating to the position ofa target pixel relative to the position of a corresponding originalpixel, and the second term relating to the pixel size of thecorresponding original pixel.
 12. The system of claim 8, the operationsfurther comprising calculating a count sum, wherein the count sum beinga sum of a count of a pixel and counts of all pixels before the pixel inthe first coordinate system; and wherein each count being a product of apixel value and a size of the original pixel.
 13. The system of claim12, calculating a value of the target pixel in the second coordinatesystem based on the lookup table and the image data relating to one ormore of the plurality of original pixels in the first coordinate systemfurther comprising based on the count sum.
 14. The system of claim 8,the first coordinate system being multi-dimensional, the operationsfurther comprising performing b) and c) for each dimension of the firstcoordinate system.
 15. A method for image processing, the methodcomprising: receiving first data of an object at a first bed positionand second data of the object at a second bed position, the first bedposition and the second bed position having an overlapping regioncorresponding to a same portion of the object; reconstructing a firstimage based on the first data; reconstructing a second image based onthe second data, a region in the at least one of the first image or thesecond image representing the same portion of the object having a signalnoise ratio (SNR) lower than a region in the at least one of the firstimage or the second image representing another portion of the object;extracting third data corresponding to the overlapping region from thefirst data; extracting fourth data corresponding to the overlappingregion from the second data, the third data and the fourth datarepresenting at least a portion of the same portion of the object;merging the third data and the fourth data to generate merged data;reconstructing a third image based on the merged data; and generating afourth image of the object through image composition based on the firstimage, the second image, and the third image, wherein the fourth imagehaving a substantially uniform SNR.
 16. The method of claim 15, thegenerating a fourth image comprising weighted composition based on afirst weight coefficient for the first image, a second weightcoefficient for the second image, and a third weight coefficient for thethird image.
 17. The method of claim 16, a sum of the first weightcoefficient, the second weight coefficient, and the third weightcoefficient being a fixed value.
 18. The method of claim 15, the firstdata, the second data, the third data, and the fourth data being savedin a sinogram mode or a listmode.
 19. The method of claim 15, furthercomprising correcting the first data, the second data, or the mergeddata.
 20. The method of claim 15, further comprising arranging themerged data based on angle or time.